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GPU acceleration of the KAZE image feature extraction algorithm

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Abstract

The recently proposed, KAZE image feature detection and description algorithm (Alcantarilla et al. in Proceedings of the British machine vision conference. LNCS, vol 7577, no 6, pp 13.1–13.11, 2013) offers significantly improved robustness in comparison to conventional algorithms like SIFT (scale-invariant feature transform) and SURF (speeded-up robust features). The improved robustness comes at a significant computational cost, however, limiting its use for many applications. We report a GPU acceleration of the KAZE algorithm that is significantly faster than its CPU counterpart. Unlike previous reports, our acceleration does not resort to binary descriptors and can serve as a drop-in replacement for CPU-KAZE, SIFT, SURF etc. By achieving nearly tenfold speedup (for a 1920 by 1200 sized image, our Compute Unified Device Architecture (CUDA)-C implementation took around 245 ms on a single GPU in comparison to nearly 2400 ms for a 16-threaded CPU version) without degradation in feature extraction performance, our work expands the applicability of the KAZE algorithm. Additionally, the strategies described here could also prove useful for the GPU implementation of other nonlinear scale-space-based image processing algorithms.

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References

  1. Alcantarilla, P., J. Davison, A., Bartoli, A.: Kaze features. In: Proceedings of the British Machine Vision Conference. LNCS. 7577(6), 13.1–13.11 (2013)

  2. Alcantarilla, P., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: Proceedings of the British Machine Vision Conference 2013, 13.1–13.11 (2013)

  3. Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: Hpatches: a benchmark and evaluation of handcrafted and learned local descriptors (2017). arXiv preprint. arXiv:1704.05939

  4. Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Google Scholar 

  5. Bianco, S., Mazzini, D., Pau, D.P., Schettini, R.: Local detectors and compact descriptors for visual search: a quantitative comparison. Digit. Signal Process. Rev. J. 44(1), 1–13 (2015)

    Google Scholar 

  6. Björkman, M., Bergström, N., Kragic, D.: Detecting, segmenting and tracking unknown objects using multi-label MRF inference. Comput. Vis. Image Underst. 118, 111–127 (2014)

    Google Scholar 

  7. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6314(4), 778–792 (2010)

    Google Scholar 

  8. Camargo, A., Papadopoulou, D., Spyropoulou, Z., Vlachonasios, K., Doonan, J.H., Gay, A.P.: Objective definition of rosette shape variation using a combined computer vision and data mining approach. PLoS ONE 9(5):e96889 (2014)

    Google Scholar 

  9. Che, S., Skadron, K.: BenchFriend: correlating the performance of GPU benchmarks. Int. J. High Perform. Comput. Appl. 28(2), 238–250 (2014)

    Google Scholar 

  10. Chen, B., Zhou, X.H., Zhang, L.W., Wang, J., Zhang, W.Q., Zhang, C.: A new nonlinear diffusion equation model for noisy image segmentation. Adv. Math. Phys. 2016 (2016)

  11. Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256–1272 (2015)

    Google Scholar 

  12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings—2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)

  13. Feng, L., Wu, Z., Long, X.: Fast image diffusion for feature detection and description. Int. J. Comput. Theory Eng. 8(1), 58–62 (2016)

    Google Scholar 

  14. Fung, J., Mann, S.: Using graphics devices in reverse: GPU-based image processing and computer vision. In: 2008 IEEE International Conference on Multimedia and Expo (ICME), pp. 9–12 (2008)

  15. Gao, X., Li, W., Loomes, M., Wang, L.: A fused deep learning architecture for viewpoint classification of echocardiography. Inf. Fusion 36, 103–113 (2017)

    Google Scholar 

  16. Gauglitz, S., Hollerer, T., Turk, M.: Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vis. 94(3), 335–360 (2011)

    MATH  Google Scholar 

  17. Grewenig, S., Weickert, J., Bruhn, A.: From box filtering to fast explicit diffusion. In: Goesele M., Roth S., Kuijper A., Schiele B., Schindler K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science (LNCS). 6376, pp. 533–542 (2010).

  18. Harvey, R.W., Bosson, A., Bangham, J.A.: A comparison of linear and non-linear scale-space filters in noise. Signal Process. VIII 1(6), 1777–1781 (1996)

    Google Scholar 

  19. Heinly, J., Dunn, E., Frahm, J.M.: Comparative evaluation of binary features. In: 12th European Conference on Computer Vision (ECCV), pp. 759–773 (2012)

  20. Hu, W., Hu, R., Xie, N., Ling, H., Maybank, S.: Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering. IEEE Trans. Image Process. 23(4), 1513–1526 (2014)

    MathSciNet  MATH  Google Scholar 

  21. Huang, H., Lu, L., Yan, B., Chen, J.: A new scale invariant feature detector and modified SURF descriptor. In: Proceedings—2010 6th International Conference on Natural Computation (ICNC). 7, pp. 3734–3738 (2010).

  22. Katzourakis, N.: Generalised solutions for fully nonlinear PDE systems and existence-uniqueness theorems. J. Differ. Equ. 263(1), 641–686 (2017)

    MathSciNet  MATH  Google Scholar 

  23. Lehiani, Y., Preda, M., Maidi, M., Ghorbel, F.: Object identification and tracking for steady registration in mobile augmented reality. In: IEEE 2015 International Conference on Signal and Image Processing Applications (ICSIPA), pp. 54–59 (2016)

  24. Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)

  25. Li, Y., Wang, S., Tian, Q., Ding, X.: A survey of recent advances in visual feature detection. Neurocomputing 149(PB), 736–751 (2015)

    Google Scholar 

  26. Lindeberg, T.: Feature detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 79–116 (1998)

    Google Scholar 

  27. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Google Scholar 

  28. Mikolajczyk, K., Mikolajczyk, K., Schmid, C., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Google Scholar 

  29. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. Int. J. Comput. Vis. 65(1), 43–72 (2005)

    Google Scholar 

  30. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Google Scholar 

  31. Pieropan, A., Björkman, M., Bergström, N., Kragic, D.: Feature descriptors for tracking by detection: a benchmark (2016). arXiv preprint. arXiv:1607.06178

  32. Rosten, E., Drummond, T.: Machine Learning for High-Speed Corner Detection. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3951, 430–443 (2006)

    Google Scholar 

  33. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)

  34. Sankaran, B., Ramalingam, S., Taguchi, Y.: Parameter learning for improving binary descriptor matching. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4892–4897 (2016)

  35. Sanna, A., Lamberti, F.: Advances in target detection and tracking in forward-looking infrared (FLIR) imagery. Sensors (Switz.) 14(11), 20297–20303 (2014)

    Google Scholar 

  36. Seung, I.P., Ponce, S.P., Huang, J., Cao, Y., Quek, F.: Low-cost, high-speed computer vision using NVIDIA’s CUDA architecture. In: Proceedingsof the 37th IEEE Applied Imagery Pattern Recognition Workshop, pp. 1-7 (2008)

  37. Tombari, F., Di Stefano, L., Mattoccia, S., Galanti, A.: Performance evaluation of robust matching measures. In: International Conference on Computer Vision Theory and Applications (VISAPP). 1, pp. 473–478 (2008)

  38. Weickert, J., Romeny, B.M.T.H., Viergever, M.A.: Efficient and reliable schemes for nonlinear diffusion filtering. IEEE Trans. Image Process. 7(3), 398–410 (1998)

    Google Scholar 

  39. Weickert, J., Scharr, H.: A scheme for coherence-enhancing diffusion filtering with optimized rotation invariance. J. Vis. Commun. Image Represent. 13(1–2), 103–118 (2002)

    Google Scholar 

  40. Zhai, Y., Ong, Y.S., Tsang, I.W.: The emerging big dimensionality. IEEE Comput. Intell. Mag. 9(3), 14–26 (2014)

    Google Scholar 

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Acknowledgements

RB and RSH acknowledge funding support from Innit Inc. (Grant no. CNS/INNIT/EE/P0210/1617/007) and High Performance Computing Lab support from Mr. Sudeep Banerjee.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology, Gandhinagar, Gujarat, India

    B. Ramkumar & Ravi Sadananda Hegde

  2. Innit Inc., Redwood City, CA, USA

    Rob Laber & Hristo Bojinov

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  1. B. Ramkumar
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  2. Rob Laber
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Correspondence to Ravi Sadananda Hegde.

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Ramkumar, B., Laber, R., Bojinov, H. et al. GPU acceleration of the KAZE image feature extraction algorithm. J Real-Time Image Proc 17, 1169–1182 (2020). https://doi.org/10.1007/s11554-019-00861-2

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